A fully-funded Research Studentship in Learning-based Control and Optimization is available at the University of Oxford.
Duration: 3.5-year D.Phil. studentship, starting in October 2025
Project: Optimal Control and Machine Learning at Scale
Supervisor: Prof. Luca Furieri
Project Overview:
The project aims to establish novel paradigms by leveraging data-driven methods and machine learning (ML) to improve the safety and performance of control architectures for large-scale dynamical systems. It will also make ML algorithms more transferable, dependable, and scalable through the lens of control theory.
The research has a strong mathematical foundation and focuses on new methodologies at the intersection of control theory and machine learning. Computational efforts will be involved to understand how large networks of physical systems behave when interfaced with online data-driven algorithms. Target benchmarks include networks of power systems and autonomous vehicles, with applications extending to federated learning and multi-agent reinforcement learning.
This project will enable the candidate to build a unique profile by integrating theoretical and engineering aspects of automatic control and ML. It provides an ideal foundation for careers in both academia and the robotics and data science industries. The candidate will collaborate with a broader team working on diverse aspects of optimization, data-driven control, and physical engineering systems. Research collaborations, both nationally and internationally, will be fostered and encouraged.
Eligibility:
This international studentship is funded through the Engineering Science department and is open to both Home and overseas students. The full award covers both fees and a stipend.
Award Value:
- Course fees: Covered at the UK student level (approximately £10,700 per year).
- Stipend: Tax-free maintenance grant of at least £19,237 per year for the first year, and at least this amount for the following two and a half years.
Candidate Requirements:
Prospective candidates will be judged on how well they meet the following criteria:
- A first-class (or strong 2:1) degree in Engineering, Mathematics, or Computer Science.
- A strong background in mathematics, control theory, machine learning, statistics, optimization, or related quantitative fields.
- A creative and rigorous mindset, and strong motivation to conduct research.
- Excellent English written and spoken communication skills.
It is desirable that candidates possess expertise in some (but not all) of the following areas:
- Control Engineering
- Applied Mathematics and Statistics
- Computer Science/AI
- Programming / Software Engineering
- Electrical Engineering or Robotics
How to Apply:
If you're interested in applying for this studentship, please contact me at luca.furieri@epfl.ch with the following documents:
- Your CV
- Grade transcripts
- A brief overview of your research interests
Please also refer to the Postgraduate Admissions guidelines. Early applications are encouraged. The final deadline is at noon on 3rd December 2024
